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Classe « KDTree »

Méthode scipy.spatial.KDTree.query

Signature de la méthode query

def query(self, x, k=1, eps=0, p=2, distance_upper_bound=inf, workers=1) 

Description

help(KDTree.query)

Query the kd-tree for nearest neighbors.

Parameters
----------
x : array_like, last dimension self.m
    An array of points to query.
k : int or Sequence[int], optional
    Either the number of nearest neighbors to return, or a list of the
    k-th nearest neighbors to return, starting from 1.
eps : nonnegative float, optional
    Return approximate nearest neighbors; the kth returned value
    is guaranteed to be no further than (1+eps) times the
    distance to the real kth nearest neighbor.
p : float, 1<=p<=infinity, optional
    Which Minkowski p-norm to use.
    1 is the sum-of-absolute-values distance ("Manhattan" distance).
    2 is the usual Euclidean distance.
    infinity is the maximum-coordinate-difference distance.
    A large, finite p may cause a ValueError if overflow can occur.
distance_upper_bound : nonnegative float, optional
    Return only neighbors within this distance. This is used to prune
    tree searches, so if you are doing a series of nearest-neighbor
    queries, it may help to supply the distance to the nearest neighbor
    of the most recent point.
workers : int, optional
    Number of workers to use for parallel processing. If -1 is given
    all CPU threads are used. Default: 1.

    .. versionadded:: 1.6.0

Returns
-------
d : float or array of floats
    The distances to the nearest neighbors.
    If ``x`` has shape ``tuple+(self.m,)``, then ``d`` has shape
    ``tuple+(k,)``.
    When k == 1, the last dimension of the output is squeezed.
    Missing neighbors are indicated with infinite distances.
    Hits are sorted by distance (nearest first).

    .. versionchanged:: 1.9.0
       Previously if ``k=None``, then `d` was an object array of
       shape ``tuple``, containing lists of distances. This behavior
       has been removed, use `query_ball_point` instead.

i : integer or array of integers
    The index of each neighbor in ``self.data``.
    ``i`` is the same shape as d.
    Missing neighbors are indicated with ``self.n``.

Examples
--------

>>> import numpy as np
>>> from scipy.spatial import KDTree
>>> x, y = np.mgrid[0:5, 2:8]
>>> tree = KDTree(np.c_[x.ravel(), y.ravel()])

To query the nearest neighbours and return squeezed result, use

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=1)
>>> print(dd, ii, sep='\n')
[2.         0.2236068]
[ 0 13]

To query the nearest neighbours and return unsqueezed result, use

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1])
>>> print(dd, ii, sep='\n')
[[2.        ]
 [0.2236068]]
[[ 0]
 [13]]

To query the second nearest neighbours and return unsqueezed result,
use

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[2])
>>> print(dd, ii, sep='\n')
[[2.23606798]
 [0.80622577]]
[[ 6]
 [19]]

To query the first and second nearest neighbours, use

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=2)
>>> print(dd, ii, sep='\n')
[[2.         2.23606798]
 [0.2236068  0.80622577]]
[[ 0  6]
 [13 19]]

or, be more specific

>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1, 2])
>>> print(dd, ii, sep='\n')
[[2.         2.23606798]
 [0.2236068  0.80622577]]
[[ 0  6]
 [13 19]]

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